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Relation Prediction Over Biomedical Knowledge Bases For Drug Repositioning, Mehmet Bakal
Relation Prediction Over Biomedical Knowledge Bases For Drug Repositioning, Mehmet Bakal
Theses and Dissertations--Computer Science
Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task …
Hadoop-Edf: Large-Scale Distributed Processing Of Electrophysiological Signal Data In Hadoop Mapreduce, Yuanyuan Wu
Hadoop-Edf: Large-Scale Distributed Processing Of Electrophysiological Signal Data In Hadoop Mapreduce, Yuanyuan Wu
Theses and Dissertations--Computer Science
The rapidly growing volume of electrophysiological signals has been generated for clinical research in neurological disorders. European Data Format (EDF) is a standard format for storing electrophysiological signals. However, the bottleneck of existing signal analysis tools for handling large-scale datasets is the sequential way of loading large EDF files before performing an analysis. To overcome this, we develop Hadoop-EDF, a distributed signal processing tool to load EDF data in a parallel manner using Hadoop MapReduce. Hadoop-EDF uses a robust data partition algorithm making EDF data parallel processable. We evaluate Hadoop-EDF’s scalability and performance by leveraging two datasets from the National …